Foundation Models in Radiology: What, How, Why, and Why Not.

Journal: Radiology
PMID: 39903075

Abstract

Recent advances in artificial intelligence have witnessed the emergence of large-scale deep learning models capable of interpreting and generating both textual and imaging data. Such models, typically referred to as foundation models (FMs), are trained on extensive corpora of unlabeled data and demonstrate high performance across various tasks. FMs have recently received extensive attention from academic, industry, and regulatory bodies. Given the potentially transformative impact that FMs can have on the field of radiology, radiologists must be aware of potential pathways to train these radiology-specific FMs, including understanding both the benefits and challenges. Thus, this review aims to explain the fundamental concepts and terms of FMs in radiology, with a specific focus on the requirements of training data, model training paradigms, model capabilities, and evaluation strategies. Overall, the goal of this review is to unify technical advances and clinical needs for safe and responsible training of FMs in radiology to ultimately benefit patients, providers, and radiologists.

Authors

  • Magdalini Paschali
    Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
  • ZhiHong Chen
    College of Information Technology and Engineering, Chengdu University, Chengdu, China.
  • Louis Blankemeier
    Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
  • Maya Varma
    Department of Computer Science, Stanford University, Stanford, California.
  • Alaa Youssef
    Department of Radiology, Stanford School of Medicine, Stanford, CA, USA.
  • Christian Bluethgen
    Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI), Stanford University, Sheffield, USA.
  • Curtis Langlotz
    School of Medicine, Stanford University, Palo Alto, CA, United States.
  • Sergios Gatidis
    Department of Radiology, Diagnostic and Interventional Radiology, Eberhard Karls University Tübingen, Germany.
  • Akshay Chaudhari
    Stanford University, Stanford, CA, USA.

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